metatdenovo

Assembly and annotation of metatranscriptomic or metagenomic data for prokaryotic, eukaryotic and viruses.

https://github.com/nf-core/metatdenovo

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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Found .zenodo.json file
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DOI references
Found 10 DOI reference(s) in README
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Scientific vocabulary similarity
Low similarity (9.4%) to scientific vocabulary

Keywords

eukaryotes metagenomics metatranscriptomics nextflow nf-core pipeline prokaryotes viruses workflow

Keywords from Contributors

dsl2 nf-test pipelines workflows 16s 18s amplicon-sequencing edna illumina iontorrent

Last synced: 6 months ago · JSON representation ·

Repository

Assembly and annotation of metatranscriptomic or metagenomic data for prokaryotic, eukaryotic and viruses.

Basic Info

Host: GitHub
Owner: nf-core
License: mit
Language: Nextflow
Default Branch: master
Homepage: https://nf-co.re/metatdenovo
Size: 103 MB

Statistics

Stars: 33
Watchers: 166
Forks: 17
Open Issues: 8
Releases: 6

Topics

eukaryotes metagenomics metatranscriptomics nextflow nf-core pipeline prokaryotes viruses workflow

Created over 4 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation

Introduction

nf-core/metatdenovo is a bioinformatics best-practice analysis pipeline for assembly and annotation of metatranscriptomic and metagenomic data from prokaryotes, eukaryotes or viruses.

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Usage

nf-core/metatdenovo metro map

Read QC (FastQC)
Present QC for raw reads (MultiQC)
Quality trimming and adapter removal for raw reads (Trim Galore!)
Optional: Filter sequences with BBduk
Optional: Normalize the sequencing depth with BBnorm
Merge trimmed, pair-end reads (Seqtk)
Choice of de novo assembly programs:
1. RNAspades suggested for both prokaryote and eukaryote assembly
2. Megahit suggested for both prokaryote and eukaryote assembly; requires less resources
Choice of orf caller:
1. TransDecoder suggested for eukaryotes; only ORFs
2. Prokka suggested for prokaryotes; ORFs and other features plus functional annotation
3. Prodigal suggested for Prokaryotes; only ORFs
Quantification of genes identified in assemblies:
1. Generate index of assembly (BBmap index)
2. Mapping cleaned reads to the assembly for quantification (BBmap)
3. Get raw counts per each gene present in the assembly (Featurecounts) -> TSV table with collected featurecounts output
Functional annotation:
1. Prokka feature identification and annotation for prokaryotes
2. eggNOG-mapper
3. KofamScan
4. HMMER search ORFs with a set of HMM profiles, and rank results
Taxonomic annotation:
1. EUKulele
2. Diamond
Summary statistics.

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

sample,fastq_1,fastq_2 sample1,./data/S1_R1_001.fastq.gz,./data/S1_R2_001.fastq.gz sample2,./data/S2_fw.fastq.gz,./data/S2_rv.fastq.gz sample3,./S4x.fastq.gz,./S4y.fastq.gz sample3,./a.fastq.gz,./b.fastq.gz

Each row represents a fastq file (single-end) or a pair of fastq files (paired-end). The fastq files need to end with .fq or .fastq, followed by .gz if gzipped. Read files from multiple rows with the same sample name will be concatenated and treated as a single sample. A mix of single-end and paired-end files is allowed, but do not mix single-end and paired-end for the same sample name.

Now, you can run the pipeline using:

bash nextflow run nf-core/metatdenovo \ -profile <docker/singularity/.../institute> \ --input samplesheet.csv \ --outdir <OUTDIR>

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

[!NOTE] Tables in the summary_tables directory under the output directory are made especially for further analysis in tools like R or Python. Their formats are standardized and column names consistent between tables.

Credits

nf-core/metatdenovo was originally written by Danilo Di Leo (@danilodileo), Emelie Nilsson (@emnilsson) & Daniel Lundin (@erikrikarddaniel).

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #metatdenovo channel (you can join with this invite).

Citations

If you use nf-core/metatdenovo for your analysis, please cite it using the following doi: 10.5281/zenodo.10666590

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

Owner

Name: nf-core
Login: nf-core
Kind: organization
Email: core@nf-co.re

Website: http://nf-co.re
Twitter: nf_core
Repositories: 84
Profile: https://github.com/nf-core

A community effort to collect a curated set of analysis pipelines built using Nextflow.

Citation (CITATIONS.md)

# nf-core/metatdenovo: Citations

## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)

> Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.

## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)

> Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.

## Pipeline tools

- [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)

> Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online].

- [MultiQC](https://pubmed.ncbi.nlm.nih.gov/27312411/)

> Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.

- [Trim Galore!](https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/)

- [khmer](https://github.com/dib-lab/khmer)

  > Brown C, Howe A, Zhang Q, Pyrkosz , and Brom T. 2012. “A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data.” ArXiv:1203.4802 [q-Bio], May. arxiv.org/abs/1203.4802.
  > Crusoe M, Alameldin H, Awad S, Boucher E, Caldwell A, Cartwright R, Charbonneau A, et al. 2015. “The Khmer Software Package: Enabling Efficient Nucleotide Sequence Analysis.” F1000Research 4 (September): 900. doi.org/10.12688/f1000research.6924.1.
  > Qingpeng Z, Pell J, Canino-Koning R, Howe A, and Brown C. 2014. “These Are Not the K-Mers You Are Looking For: Efficient Online K-Mer Counting Using a Probabilistic Data Structure.” PLOS ONE 9 (7): e101271. doi.org/10.1371/journal.pone.0101271.

- [Seqtk](https://github.com/lh3/seqtk)

- [RNAspade](https://cab.spbu.ru/software/rnaspades/)

  > Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012 May;19(5):455-77. doi: 10.1089/cmb.2012.0021.
  > Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017 May;27(5):824-834. doi: 10.1101/gr.213959.116. Epub 2017 Mar 15.
  > Bushmanova E, Antipov D, Lapidus A, Prjibelski A, rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data GigaScience, 2019
  > Prjibelski, A., Antipov, D., Meleshko, D., Lapidus, A., & Korobeynikov, A. (2020). “Using SPAdes de novo assembler.” Current Protocols in Bioinformatics, 70, e102. doi: 10.1002/cpbi.102
  > Antipov D, Raiko M, Lapidus A, Pevzner P, METAVIRALSPADES: assembly of viruses from metagenomic data, Bioinformatics, Volume 36, Issue 14, July 2020, Pages 4126–4129, https://doi.org/10.1093/bioinformatics/btaa490

- [Megahit](https://github.com/voutcn/megahit)

  > Li D, Liu C, Luo R, Sadakane K, and Lam T, (2015) MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics, doi: 10.1093/bioinformatics/btv033 [PMID: 25609793].
  > Li D, Luo R, Liu C, Leung C, Ting H, Sadakane K, Yamashita H and Lam T, 2016. MEGAHIT v1.0: A Fast and Scalable Metagenome Assembler driven by Advanced Methodologies and Community Practices. Methods.

- [TransDecoder](https://github.com/TransDecoder/TransDecoder)

- [Prokka](https://github.com/tseemann/prokka)

  > Seemann T Prokka: rapid prokaryotic genome annotation Bioinformatics 2014 Jul 15;30(14):2068-9. PMID:24642063

- [Prodigal](https://github.com/hyattpd/Prodigal)

- [BBmap](https://sourceforge.net/projects/bbmap/)

- [FeatureCounts](https://subread.sourceforge.net)

  > Liao Y, Smyth GK and Shi W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Research, 47(8):e47, 2019
  > Liao Y, Smyth GK and Shi W. featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7):923-30, 2014
  > Liao Y, Smyth GK and Shi W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Research, 41(10):e108, 2013

- [Eggnog](https://github.com/eggnogdb/eggnog-mapper)

  > Cantalapiedra C, Hernandez-Plaza A, Letunic I, Bork P, Huerta-Cepas J.
  > eggNOG-mapper v2: functional annotation, orthology assignments, and domain
  > prediction at the metagenomic scale 2021.
  > Molecular Biology and Evolution, msab293, https://doi.org/10.1093/molbev/msab293
  > eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated
  > orthology resource based on 5090 organisms and 2502 viruses. Jaime
  > Huerta-Cepas, Damian Szklarczyk, Davide Heller, Ana Hernández-Plaza, Sofia
  > K Forslund, Helen Cook, Daniel R Mende, Ivica Letunic, Thomas Rattei, Lars
  > J Jensen, Christian von Mering, Peer Bork Nucleic Acids Res. 2019 Jan 8;
  > 47(Database issue): D309–D314. doi: 10.1093/nar/gky1085

- [Kofamscan](https://github.com/takaram/kofam_scan)

- [HMMsearch](https://www.ebi.ac.uk/Tools/hmmer/search/hmmsearch)

- [EUKulele](https://github.com/AlexanderLabWHOI/EUKulele)

- [Diamond](https://github.com/bbuchfink/diamond)

  > Buchfink B, Xie C, and Huson D. 2015. “Fast and Sensitive Protein Alignment Using DIAMOND.” Nature Methods 12 (1): 59–60. https://doi.org/10.1038/nmeth.3176.

- [TaxonKit](https://bioinf.shenwei.me/taxonkit/)

  > Wei S, Ren H. 2021. “TaxonKit: A Practical and Efficient NCBI Taxonomy Toolkit.” Journal of Genetics and Genomics, Special issue on Microbiome, 48 (9): 844–50. https://doi.org/10.1016/j.jgg.2021.03.006.

- [CAT](https://github.com/dutilh/CAT)

  > von Meijenfeldt FAB, Arkhipova K, Cambuy DD, Coutinho FH, Dutilh BE. Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biology. 2019;20:217.

- [transrate](https://hibberdlab.com/transrate/)

  > Smith-Unna R, Boursnell C, Patro R, Hibberd J, Kelly S. TransRate: reference free quality assessment of de-novo transcriptome assemblies (2016). Genome Research doi: [http://dx.doi.org/10.1101/gr.196469.115](http://dx.doi.org/10.1101/gr.196469.115)

## Software packaging/containerisation tools

- [Anaconda](https://anaconda.com)

  > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.

- [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/)

  > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.

- [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)

  > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.

- [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241)

  > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.

- [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/)

  > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.

GitHub Events

Total

Create event: 16
Release event: 4
Issues event: 34
Watch event: 11
Delete event: 11
Issue comment event: 113
Push event: 91
Pull request review comment event: 70
Pull request event: 121
Pull request review event: 103
Fork event: 3

Last Year

Create event: 16
Release event: 4
Issues event: 34
Watch event: 11
Delete event: 11
Issue comment event: 113
Push event: 91
Pull request review comment event: 70
Pull request event: 121
Pull request review event: 103
Fork event: 3

Committers

Last synced: over 2 years ago

All Time

Total Commits: 710
Total Committers: 8
Avg Commits per committer: 88.75
Development Distribution Score (DDS): 0.418

Past Year

Commits: 494
Committers: 7
Avg Commits per committer: 70.571
Development Distribution Score (DDS): 0.451

Top Committers

Name	Email	Commits
Danilo Di Leo	d**o@l**e	413
Danilo Di Leo	7****o	121
Daniel Lundin	e**l@g**m	76
Emelie Nilsson	e**n@l**e	43
Daniel Lundin	m**s@g**m	35
Taylor Falk	t**k@a**m	14
Danilo Di Leo	7****1	7
tfalkarkea	1****a	1

Committer Domains (Top 20 + Academic)

lnu.se: 2 arkeabio.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 84
Total pull requests: 160
Average time to close issues: about 2 months
Average time to close pull requests: 5 days
Total issue authors: 12
Total pull request authors: 8
Average comments per issue: 1.51
Average comments per pull request: 1.09
Merged pull requests: 128
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 19
Pull requests: 67
Average time to close issues: 22 days
Average time to close pull requests: 1 day
Issue authors: 8
Pull request authors: 6
Average comments per issue: 0.74
Average comments per pull request: 0.94
Merged pull requests: 44
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

erikrikarddaniel (51)
danilodileo (23)
emnilsson (8)
m3hdad (3)
luciazifcakova (3)
wresch (2)
BenPonBiobrain (2)
anhong11 (1)
tfalkarkea (1)
geweaa (1)
petemeng (1)
maxulysse (1)
jen-reeve (1)
adriaaula (1)
edmundmiller (1)

Pull Request Authors

danilodileo (114)
erikrikarddaniel (65)
nf-core-bot (17)
tfalkarkea (3)
m3hdad (2)
herich0 (2)
dslarm (1)
emnilsson (1)
maxulysse (1)

Top Labels

Issue Labels

bug (53) enhancement (44) question (2) good first issue (1)

Pull Request Labels

Dependencies

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modules/local/format/spades/environment.yml conda

gzip 1.11.*

modules/local/hmmrank/environment.yml conda

r-data.table 1.14.8.*
r-dtplyr 1.3.1.*
r-tidyverse 2.0.0.*

modules/local/kofamscan/download/environment.yml conda

wget 1.21.4.*

modules/local/kofamscan/scan/environment.yml conda

kofamscan 1.3.0.*

modules/local/kofamscan/sum/environment.yml conda

r-data.table 1.14.8.*
r-dtplyr 1.3.1.*
r-tidyverse 2.0.0.*

modules/local/kofamscan/unique/environment.yml conda

r-data.table 1.14.8.*
r-dtplyr 1.3.1.*
r-tidyverse 2.0.0.*

modules/local/megahit/interleaved/environment.yml conda

megahit 1.2.9.*
pigz 2.6.*

modules/local/merge/summary/environment.yml conda

r-data.table 1.14.8.*
r-dtplyr 1.3.1.*
r-tidyverse 2.0.0.*

modules/local/prokka/gff2tsv/environment.yml conda

r-data.table 1.14.8.*
r-dtplyr 1.3.1.*
r-tidyverse 2.0.0.*

modules/local/seqtk/hmmhitfaas/environment.yml conda

seqtk 1.4.*

modules/local/spades/writeyaml/environment.yml conda

pigz 2.3.4.*

modules/local/sumtaxonomy/environment.yml conda

r-data.table 1.14.8.*
r-dtplyr 1.3.1.*
r-tidyverse 2.0.0.*

modules/local/transdecoder/environment.yml conda

transdecoder 5.7.1.*

modules/local/transrate/environment.yml conda

transrate 1.0.3.*

metatdenovo

Science Score: 57.0%

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